{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "127dd72e-7ed6-4e6b-8864-be0b479ccf2e",
   "metadata": {},
   "source": [
    "# Train Image Classifiers\n",
    "\n",
    "In this notebook we will train an image classifier that classify fruit images, using MMClassificaiton."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22298ae9-0024-4e86-a903-4981053f5424",
   "metadata": {},
   "source": [
    "## Prepare a Dataset\n",
    "\n",
    "We have already prepared a dataset.\n",
    "\n",
    "Credit to Zihao: https://github.com/TommyZihao/MMClassification_Tutorials\n",
    "\n",
    "To download and extract the dataset, in command line:\n",
    "```\n",
    "curl -O https://zihao-openmmlab.obs.myhuaweicloud.com/20220716-mmclassification/dataset/fruit30/fruit30_split.zip\n",
    "unzip -d data fruit30_split.zip\n",
    "```\n",
    "\n",
    "The dataset should be categorized by folders, for MMClassification to read."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dd9fc5f-d7a7-4bf4-8c43-ab7156e81831",
   "metadata": {},
   "source": [
    "## Prepare a Config and Checkpoint File\n",
    "\n",
    "For speed consideration, we use a lightweight neural network, MobileNetV2.\n",
    "\n",
    "we use mim to download the config file and checkpoint file.\n",
    "\n",
    "```\n",
    "mim download mmcls --config mobilenet-v2_8xb32_in1k --dest .\n",
    "mv mobilenet-v2_8xb32_in1k.py mobilenet-v2_fruit.py\n",
    "```\n",
    "\n",
    "If you prefer to play with other models, navigate to [MMClassification model zoo](https://mmclassification.readthedocs.io/en/latest/model_zoo.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "76ca0906-acf9-4096-878a-7504e0d0622a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing mobilenet-v2_8xb32_in1k...\r\n",
      "\u001B[32mmobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth exists in /home/cine/Documents/sjtu-openmmlab-tutorial-main\u001B[0m\r\n",
      "\u001B[32mSuccessfully dumped mobilenet-v2_8xb32_in1k.py to /home/cine/Documents/sjtu-openmmlab-tutorial-main\u001B[0m\r\n"
     ]
    }
   ],
   "source": [
    "!mim download mmcls --config mobilenet-v2_8xb32_in1k --dest .\n",
    "!mv mobilenet-v2_8xb32_in1k.py mobilenet-v2_fruit.py"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6e6ada9-8978-495c-86e2-a965799ddbb9",
   "metadata": {},
   "source": [
    "## Modify the Config File\n",
    "\n",
    "1. Remove some intermediate item for clean: `dataset_type`, `img_norm_cfg`, `train_pipeline`, `test_pipeline`\n",
    "1. Modify model\n",
    "    1. number of class: from 1000 to 30\n",
    "    2. pretrain weights: from None to the downloaded checkpoint file, as we finetune the model instead of training from scratch\n",
    "1. Data: for train/val/test \n",
    "    1. `type`: `ImageNet` -> `CustomDataset`\n",
    "    2. `prefix`, which is the root path to images: modify to `\"data/fruit30_split/train\"` or `\"data/fruit30_split/val\"`\n",
    "    3. `ann_file`, use folder name as class name: modify to `None`\n",
    "1. Runner and Optimizer\n",
    "    1. number of training epochs: `runner.max_epochs`\n",
    "    1. learning rates: `optimizer.lr`, usually divided by 8 due to linear scaling rules.\n",
    "1. Misc\n",
    "    1. Decrease `log_confg.interval` for small computation power\n",
    "    1. Increase `checkpoint_config.interval` to avoid saving too many checkpoint, to same time and disk space\n",
    "1. Further parameter tuning you may try\n",
    "    1. learning rates: Decrease `optimizer.lr` for finetuning \n",
    "    1. configure learning scheduler to decrease learning when loss saturates. Moreover, by setting `by_epoch=False`, we decrease learning rate by iteration instead of by epoches.\n",
    "    1. Monitor loss decrease and re-tune\n",
    "    1. More available lr_schedulers are available in [mmcv](https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/lr_updater.py)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8071497-dd81-4121-b2b0-91df11d41734",
   "metadata": {},
   "source": [
    "## Launch Training\n",
    "\n",
    "In command line\n",
    "\n",
    "```\n",
    "mim train mmcls mobilenet-v2_fruit.py\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0b69849c-3f89-4446-b9de-970e7f47f33e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/cine/miniconda3/envs/mmlab1/lib/python3.8/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details.\r\n",
      "  warnings.warn(\r\n",
      "Training command is /home/cine/miniconda3/envs/mmlab1/bin/python /home/cine/mmclassification/mmcls/.mim/tools/train.py mobilenet-v2_fruit.py --launcher none --gpus 1. \r\n",
      "/home/cine/miniconda3/envs/mmlab1/lib/python3.8/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details.\r\n",
      "  warnings.warn(\r\n",
      "/home/cine/mmclassification/mmcls/utils/setup_env.py:32: UserWarning: Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.\r\n",
      "  warnings.warn(\r\n",
      "/home/cine/mmclassification/mmcls/utils/setup_env.py:42: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.\r\n",
      "  warnings.warn(\r\n",
      "/home/cine/mmclassification/mmcls/.mim/tools/train.py:116: UserWarning: `--gpus` is deprecated because we only support single GPU mode in non-distributed training. Use `gpus=1` now.\r\n",
      "  warnings.warn('`--gpus` is deprecated because we only support '\r\n",
      "fatal: not a git repository (or any of the parent directories): .git\r\n",
      "2023-02-03 23:28:49,376 - mmcls - INFO - Environment info:\r\n",
      "------------------------------------------------------------\r\n",
      "sys.platform: linux\r\n",
      "Python: 3.8.16 (default, Jan 17 2023, 23:13:24) [GCC 11.2.0]\r\n",
      "CUDA available: True\r\n",
      "GPU 0: NVIDIA GeForce GTX 1080 Ti\r\n",
      "CUDA_HOME: /home/cine/miniconda3/envs/mmlab1\r\n",
      "NVCC: Cuda compilation tools, release 11.6, V11.6.124\r\n",
      "GCC: gcc (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0\r\n",
      "PyTorch: 1.6.0\r\n",
      "PyTorch compiling details: PyTorch built with:\r\n",
      "  - GCC 7.3\r\n",
      "  - C++ Version: 201402\r\n",
      "  - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications\r\n",
      "  - Intel(R) MKL-DNN v1.5.0 (Git Hash e2ac1fac44c5078ca927cb9b90e1b3066a0b2ed0)\r\n",
      "  - OpenMP 201511 (a.k.a. OpenMP 4.5)\r\n",
      "  - NNPACK is enabled\r\n",
      "  - CPU capability usage: AVX2\r\n",
      "  - CUDA Runtime 10.2\r\n",
      "  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_37,code=compute_37\r\n",
      "  - CuDNN 7.6.5\r\n",
      "  - Magma 2.5.2\r\n",
      "  - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_STATIC_DISPATCH=OFF, \r\n",
      "\r\n",
      "TorchVision: 0.7.0\r\n",
      "OpenCV: 4.7.0\r\n",
      "MMCV: 1.7.1\r\n",
      "MMCV Compiler: GCC 7.3\r\n",
      "MMCV CUDA Compiler: 10.2\r\n",
      "MMClassification: 0.25.0+\r\n",
      "------------------------------------------------------------\r\n",
      "\r\n",
      "2023-02-03 23:28:49,377 - mmcls - INFO - Distributed training: False\r\n",
      "2023-02-03 23:28:49,440 - mmcls - INFO - Config:\r\n",
      "model = dict(\r\n",
      "    type='ImageClassifier',\r\n",
      "    backbone=dict(type='MobileNetV2', widen_factor=1.0),\r\n",
      "    neck=dict(type='GlobalAveragePooling'),\r\n",
      "    head=dict(\r\n",
      "        type='LinearClsHead',\r\n",
      "        num_classes=30,\r\n",
      "        in_channels=1280,\r\n",
      "        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),\r\n",
      "        topk=(1, 5)))\r\n",
      "load_from = 'mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth'\r\n",
      "data = dict(\r\n",
      "    samples_per_gpu=32,\r\n",
      "    workers_per_gpu=2,\r\n",
      "    train=dict(\r\n",
      "        type='CustomDataset',\r\n",
      "        data_prefix='data/fruit30_split/train',\r\n",
      "        pipeline=[\r\n",
      "            dict(type='LoadImageFromFile'),\r\n",
      "            dict(type='RandomResizedCrop', size=224, backend='pillow'),\r\n",
      "            dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),\r\n",
      "            dict(\r\n",
      "                type='Normalize',\r\n",
      "                mean=[123.675, 116.28, 103.53],\r\n",
      "                std=[58.395, 57.12, 57.375],\r\n",
      "                to_rgb=True),\r\n",
      "            dict(type='ImageToTensor', keys=['img']),\r\n",
      "            dict(type='ToTensor', keys=['gt_label']),\r\n",
      "            dict(type='Collect', keys=['img', 'gt_label'])\r\n",
      "        ]),\r\n",
      "    val=dict(\r\n",
      "        type='CustomDataset',\r\n",
      "        data_prefix='data/fruit30_split/val',\r\n",
      "        pipeline=[\r\n",
      "            dict(type='LoadImageFromFile'),\r\n",
      "            dict(type='Resize', size=(256, -1), backend='pillow'),\r\n",
      "            dict(type='CenterCrop', crop_size=224),\r\n",
      "            dict(\r\n",
      "                type='Normalize',\r\n",
      "                mean=[123.675, 116.28, 103.53],\r\n",
      "                std=[58.395, 57.12, 57.375],\r\n",
      "                to_rgb=True),\r\n",
      "            dict(type='ImageToTensor', keys=['img']),\r\n",
      "            dict(type='Collect', keys=['img'])\r\n",
      "        ]),\r\n",
      "    test=dict(\r\n",
      "        type='CustomDataset',\r\n",
      "        data_prefix='data/fruit30_split/val',\r\n",
      "        pipeline=[\r\n",
      "            dict(type='LoadImageFromFile'),\r\n",
      "            dict(type='Resize', size=(256, -1), backend='pillow'),\r\n",
      "            dict(type='CenterCrop', crop_size=224),\r\n",
      "            dict(\r\n",
      "                type='Normalize',\r\n",
      "                mean=[123.675, 116.28, 103.53],\r\n",
      "                std=[58.395, 57.12, 57.375],\r\n",
      "                to_rgb=True),\r\n",
      "            dict(type='ImageToTensor', keys=['img']),\r\n",
      "            dict(type='Collect', keys=['img'])\r\n",
      "        ]))\r\n",
      "evaluation = dict(interval=1, metric='accuracy')\r\n",
      "optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=4e-05)\r\n",
      "optimizer_config = dict(grad_clip=None)\r\n",
      "lr_config = dict(policy='step', gamma=0.98, step=1)\r\n",
      "runner = dict(type='EpochBasedRunner', max_epochs=5)\r\n",
      "checkpoint_config = dict(interval=5)\r\n",
      "log_config = dict(interval=10, hooks=[dict(type='TextLoggerHook')])\r\n",
      "dist_params = dict(backend='nccl')\r\n",
      "log_level = 'INFO'\r\n",
      "resume_from = None\r\n",
      "workflow = [('train', 1)]\r\n",
      "work_dir = './work_dirs/mobilenet-v2_fruit'\r\n",
      "gpu_ids = range(0, 1)\r\n",
      "\r\n",
      "2023-02-03 23:28:49,440 - mmcls - INFO - Set random seed to 1802436253, deterministic: False\r\n",
      "2023-02-03 23:28:49,489 - mmcv - INFO - initialize MobileNetV2 with init_cfg [{'type': 'Kaiming', 'layer': ['Conv2d']}, {'type': 'Constant', 'val': 1, 'layer': ['_BatchNorm', 'GroupNorm']}]\r\n",
      "2023-02-03 23:28:49,528 - mmcv - INFO - initialize LinearClsHead with init_cfg {'type': 'Normal', 'layer': 'Linear', 'std': 0.01}\r\n",
      "2023-02-03 23:28:49,528 - mmcv - INFO - \r\n",
      "backbone.conv1.conv.weight - torch.Size([32, 3, 3, 3]): \r\n",
      "Initialized by user-defined `init_weights` in ConvModule  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,528 - mmcv - INFO - \r\n",
      "backbone.conv1.bn.weight - torch.Size([32]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,528 - mmcv - INFO - \r\n",
      "backbone.conv1.bn.bias - torch.Size([32]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,528 - mmcv - INFO - \r\n",
      "backbone.layer1.0.conv.0.conv.weight - torch.Size([32, 1, 3, 3]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,528 - mmcv - INFO - \r\n",
      "backbone.layer1.0.conv.0.bn.weight - torch.Size([32]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,528 - mmcv - INFO - \r\n",
      "backbone.layer1.0.conv.0.bn.bias - torch.Size([32]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,528 - mmcv - INFO - \r\n",
      "backbone.layer1.0.conv.1.conv.weight - torch.Size([16, 32, 1, 1]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,528 - mmcv - INFO - \r\n",
      "backbone.layer1.0.conv.1.bn.weight - torch.Size([16]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,528 - mmcv - INFO - \r\n",
      "backbone.layer1.0.conv.1.bn.bias - torch.Size([16]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,528 - mmcv - INFO - \r\n",
      "backbone.layer2.0.conv.0.conv.weight - torch.Size([96, 16, 1, 1]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,529 - mmcv - INFO - \r\n",
      "backbone.layer2.0.conv.0.bn.weight - torch.Size([96]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,529 - mmcv - INFO - \r\n",
      "backbone.layer2.0.conv.0.bn.bias - torch.Size([96]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,529 - mmcv - INFO - \r\n",
      "backbone.layer2.0.conv.1.conv.weight - torch.Size([96, 1, 3, 3]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,529 - mmcv - INFO - \r\n",
      "backbone.layer2.0.conv.1.bn.weight - torch.Size([96]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,529 - mmcv - INFO - \r\n",
      "backbone.layer2.0.conv.1.bn.bias - torch.Size([96]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,529 - mmcv - INFO - \r\n",
      "backbone.layer2.0.conv.2.conv.weight - torch.Size([24, 96, 1, 1]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,529 - mmcv - INFO - \r\n",
      "backbone.layer2.0.conv.2.bn.weight - torch.Size([24]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,529 - mmcv - INFO - \r\n",
      "backbone.layer2.0.conv.2.bn.bias - torch.Size([24]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,529 - mmcv - INFO - \r\n",
      "backbone.layer2.1.conv.0.conv.weight - torch.Size([144, 24, 1, 1]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,529 - mmcv - INFO - \r\n",
      "backbone.layer2.1.conv.0.bn.weight - torch.Size([144]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,529 - mmcv - INFO - \r\n",
      "backbone.layer2.1.conv.0.bn.bias - torch.Size([144]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,529 - mmcv - INFO - \r\n",
      "backbone.layer2.1.conv.1.conv.weight - torch.Size([144, 1, 3, 3]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,529 - mmcv - INFO - \r\n",
      "backbone.layer2.1.conv.1.bn.weight - torch.Size([144]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,529 - mmcv - INFO - \r\n",
      "backbone.layer2.1.conv.1.bn.bias - torch.Size([144]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,529 - mmcv - INFO - \r\n",
      "backbone.layer2.1.conv.2.conv.weight - torch.Size([24, 144, 1, 1]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,529 - mmcv - INFO - \r\n",
      "backbone.layer2.1.conv.2.bn.weight - torch.Size([24]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,529 - mmcv - INFO - \r\n",
      "backbone.layer2.1.conv.2.bn.bias - torch.Size([24]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,529 - mmcv - INFO - \r\n",
      "backbone.layer3.0.conv.0.conv.weight - torch.Size([144, 24, 1, 1]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,529 - mmcv - INFO - \r\n",
      "backbone.layer3.0.conv.0.bn.weight - torch.Size([144]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,529 - mmcv - INFO - \r\n",
      "backbone.layer3.0.conv.0.bn.bias - torch.Size([144]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,529 - mmcv - INFO - \r\n",
      "backbone.layer3.0.conv.1.conv.weight - torch.Size([144, 1, 3, 3]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,529 - mmcv - INFO - \r\n",
      "backbone.layer3.0.conv.1.bn.weight - torch.Size([144]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,529 - mmcv - INFO - \r\n",
      "backbone.layer3.0.conv.1.bn.bias - torch.Size([144]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,529 - mmcv - INFO - \r\n",
      "backbone.layer3.0.conv.2.conv.weight - torch.Size([32, 144, 1, 1]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,529 - mmcv - INFO - \r\n",
      "backbone.layer3.0.conv.2.bn.weight - torch.Size([32]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,529 - mmcv - INFO - \r\n",
      "backbone.layer3.0.conv.2.bn.bias - torch.Size([32]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,529 - mmcv - INFO - \r\n",
      "backbone.layer3.1.conv.0.conv.weight - torch.Size([192, 32, 1, 1]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,529 - mmcv - INFO - \r\n",
      "backbone.layer3.1.conv.0.bn.weight - torch.Size([192]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,529 - mmcv - INFO - \r\n",
      "backbone.layer3.1.conv.0.bn.bias - torch.Size([192]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,529 - mmcv - INFO - \r\n",
      "backbone.layer3.1.conv.1.conv.weight - torch.Size([192, 1, 3, 3]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,529 - mmcv - INFO - \r\n",
      "backbone.layer3.1.conv.1.bn.weight - torch.Size([192]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,529 - mmcv - INFO - \r\n",
      "backbone.layer3.1.conv.1.bn.bias - torch.Size([192]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,529 - mmcv - INFO - \r\n",
      "backbone.layer3.1.conv.2.conv.weight - torch.Size([32, 192, 1, 1]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,529 - mmcv - INFO - \r\n",
      "backbone.layer3.1.conv.2.bn.weight - torch.Size([32]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,530 - mmcv - INFO - \r\n",
      "backbone.layer3.1.conv.2.bn.bias - torch.Size([32]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,530 - mmcv - INFO - \r\n",
      "backbone.layer3.2.conv.0.conv.weight - torch.Size([192, 32, 1, 1]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,530 - mmcv - INFO - \r\n",
      "backbone.layer3.2.conv.0.bn.weight - torch.Size([192]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,530 - mmcv - INFO - \r\n",
      "backbone.layer3.2.conv.0.bn.bias - torch.Size([192]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,530 - mmcv - INFO - \r\n",
      "backbone.layer3.2.conv.1.conv.weight - torch.Size([192, 1, 3, 3]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,530 - mmcv - INFO - \r\n",
      "backbone.layer3.2.conv.1.bn.weight - torch.Size([192]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,530 - mmcv - INFO - \r\n",
      "backbone.layer3.2.conv.1.bn.bias - torch.Size([192]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,530 - mmcv - INFO - \r\n",
      "backbone.layer3.2.conv.2.conv.weight - torch.Size([32, 192, 1, 1]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,530 - mmcv - INFO - \r\n",
      "backbone.layer3.2.conv.2.bn.weight - torch.Size([32]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,530 - mmcv - INFO - \r\n",
      "backbone.layer3.2.conv.2.bn.bias - torch.Size([32]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,530 - mmcv - INFO - \r\n",
      "backbone.layer4.0.conv.0.conv.weight - torch.Size([192, 32, 1, 1]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,530 - mmcv - INFO - \r\n",
      "backbone.layer4.0.conv.0.bn.weight - torch.Size([192]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,530 - mmcv - INFO - \r\n",
      "backbone.layer4.0.conv.0.bn.bias - torch.Size([192]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,530 - mmcv - INFO - \r\n",
      "backbone.layer4.0.conv.1.conv.weight - torch.Size([192, 1, 3, 3]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,530 - mmcv - INFO - \r\n",
      "backbone.layer4.0.conv.1.bn.weight - torch.Size([192]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,530 - mmcv - INFO - \r\n",
      "backbone.layer4.0.conv.1.bn.bias - torch.Size([192]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,530 - mmcv - INFO - \r\n",
      "backbone.layer4.0.conv.2.conv.weight - torch.Size([64, 192, 1, 1]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,530 - mmcv - INFO - \r\n",
      "backbone.layer4.0.conv.2.bn.weight - torch.Size([64]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,530 - mmcv - INFO - \r\n",
      "backbone.layer4.0.conv.2.bn.bias - torch.Size([64]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,530 - mmcv - INFO - \r\n",
      "backbone.layer4.1.conv.0.conv.weight - torch.Size([384, 64, 1, 1]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,530 - mmcv - INFO - \r\n",
      "backbone.layer4.1.conv.0.bn.weight - torch.Size([384]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,530 - mmcv - INFO - \r\n",
      "backbone.layer4.1.conv.0.bn.bias - torch.Size([384]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,530 - mmcv - INFO - \r\n",
      "backbone.layer4.1.conv.1.conv.weight - torch.Size([384, 1, 3, 3]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,530 - mmcv - INFO - \r\n",
      "backbone.layer4.1.conv.1.bn.weight - torch.Size([384]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,530 - mmcv - INFO - \r\n",
      "backbone.layer4.1.conv.1.bn.bias - torch.Size([384]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,530 - mmcv - INFO - \r\n",
      "backbone.layer4.1.conv.2.conv.weight - torch.Size([64, 384, 1, 1]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,530 - mmcv - INFO - \r\n",
      "backbone.layer4.1.conv.2.bn.weight - torch.Size([64]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,530 - mmcv - INFO - \r\n",
      "backbone.layer4.1.conv.2.bn.bias - torch.Size([64]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,530 - mmcv - INFO - \r\n",
      "backbone.layer4.2.conv.0.conv.weight - torch.Size([384, 64, 1, 1]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,530 - mmcv - INFO - \r\n",
      "backbone.layer4.2.conv.0.bn.weight - torch.Size([384]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,530 - mmcv - INFO - \r\n",
      "backbone.layer4.2.conv.0.bn.bias - torch.Size([384]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,530 - mmcv - INFO - \r\n",
      "backbone.layer4.2.conv.1.conv.weight - torch.Size([384, 1, 3, 3]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,530 - mmcv - INFO - \r\n",
      "backbone.layer4.2.conv.1.bn.weight - torch.Size([384]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,530 - mmcv - INFO - \r\n",
      "backbone.layer4.2.conv.1.bn.bias - torch.Size([384]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,530 - mmcv - INFO - \r\n",
      "backbone.layer4.2.conv.2.conv.weight - torch.Size([64, 384, 1, 1]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,530 - mmcv - INFO - \r\n",
      "backbone.layer4.2.conv.2.bn.weight - torch.Size([64]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,531 - mmcv - INFO - \r\n",
      "backbone.layer4.2.conv.2.bn.bias - torch.Size([64]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,531 - mmcv - INFO - \r\n",
      "backbone.layer4.3.conv.0.conv.weight - torch.Size([384, 64, 1, 1]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,531 - mmcv - INFO - \r\n",
      "backbone.layer4.3.conv.0.bn.weight - torch.Size([384]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,531 - mmcv - INFO - \r\n",
      "backbone.layer4.3.conv.0.bn.bias - torch.Size([384]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,531 - mmcv - INFO - \r\n",
      "backbone.layer4.3.conv.1.conv.weight - torch.Size([384, 1, 3, 3]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,531 - mmcv - INFO - \r\n",
      "backbone.layer4.3.conv.1.bn.weight - torch.Size([384]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,531 - mmcv - INFO - \r\n",
      "backbone.layer4.3.conv.1.bn.bias - torch.Size([384]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,531 - mmcv - INFO - \r\n",
      "backbone.layer4.3.conv.2.conv.weight - torch.Size([64, 384, 1, 1]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,531 - mmcv - INFO - \r\n",
      "backbone.layer4.3.conv.2.bn.weight - torch.Size([64]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,531 - mmcv - INFO - \r\n",
      "backbone.layer4.3.conv.2.bn.bias - torch.Size([64]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,531 - mmcv - INFO - \r\n",
      "backbone.layer5.0.conv.0.conv.weight - torch.Size([384, 64, 1, 1]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,531 - mmcv - INFO - \r\n",
      "backbone.layer5.0.conv.0.bn.weight - torch.Size([384]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,531 - mmcv - INFO - \r\n",
      "backbone.layer5.0.conv.0.bn.bias - torch.Size([384]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,531 - mmcv - INFO - \r\n",
      "backbone.layer5.0.conv.1.conv.weight - torch.Size([384, 1, 3, 3]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,531 - mmcv - INFO - \r\n",
      "backbone.layer5.0.conv.1.bn.weight - torch.Size([384]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,531 - mmcv - INFO - \r\n",
      "backbone.layer5.0.conv.1.bn.bias - torch.Size([384]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,531 - mmcv - INFO - \r\n",
      "backbone.layer5.0.conv.2.conv.weight - torch.Size([96, 384, 1, 1]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,531 - mmcv - INFO - \r\n",
      "backbone.layer5.0.conv.2.bn.weight - torch.Size([96]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,531 - mmcv - INFO - \r\n",
      "backbone.layer5.0.conv.2.bn.bias - torch.Size([96]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,531 - mmcv - INFO - \r\n",
      "backbone.layer5.1.conv.0.conv.weight - torch.Size([576, 96, 1, 1]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,531 - mmcv - INFO - \r\n",
      "backbone.layer5.1.conv.0.bn.weight - torch.Size([576]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,531 - mmcv - INFO - \r\n",
      "backbone.layer5.1.conv.0.bn.bias - torch.Size([576]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,531 - mmcv - INFO - \r\n",
      "backbone.layer5.1.conv.1.conv.weight - torch.Size([576, 1, 3, 3]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,531 - mmcv - INFO - \r\n",
      "backbone.layer5.1.conv.1.bn.weight - torch.Size([576]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,531 - mmcv - INFO - \r\n",
      "backbone.layer5.1.conv.1.bn.bias - torch.Size([576]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,531 - mmcv - INFO - \r\n",
      "backbone.layer5.1.conv.2.conv.weight - torch.Size([96, 576, 1, 1]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,531 - mmcv - INFO - \r\n",
      "backbone.layer5.1.conv.2.bn.weight - torch.Size([96]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,531 - mmcv - INFO - \r\n",
      "backbone.layer5.1.conv.2.bn.bias - torch.Size([96]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,532 - mmcv - INFO - \r\n",
      "backbone.layer5.2.conv.0.conv.weight - torch.Size([576, 96, 1, 1]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,532 - mmcv - INFO - \r\n",
      "backbone.layer5.2.conv.0.bn.weight - torch.Size([576]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,532 - mmcv - INFO - \r\n",
      "backbone.layer5.2.conv.0.bn.bias - torch.Size([576]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,532 - mmcv - INFO - \r\n",
      "backbone.layer5.2.conv.1.conv.weight - torch.Size([576, 1, 3, 3]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,532 - mmcv - INFO - \r\n",
      "backbone.layer5.2.conv.1.bn.weight - torch.Size([576]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,532 - mmcv - INFO - \r\n",
      "backbone.layer5.2.conv.1.bn.bias - torch.Size([576]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,532 - mmcv - INFO - \r\n",
      "backbone.layer5.2.conv.2.conv.weight - torch.Size([96, 576, 1, 1]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,532 - mmcv - INFO - \r\n",
      "backbone.layer5.2.conv.2.bn.weight - torch.Size([96]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,532 - mmcv - INFO - \r\n",
      "backbone.layer5.2.conv.2.bn.bias - torch.Size([96]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,532 - mmcv - INFO - \r\n",
      "backbone.layer6.0.conv.0.conv.weight - torch.Size([576, 96, 1, 1]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,532 - mmcv - INFO - \r\n",
      "backbone.layer6.0.conv.0.bn.weight - torch.Size([576]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,532 - mmcv - INFO - \r\n",
      "backbone.layer6.0.conv.0.bn.bias - torch.Size([576]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,532 - mmcv - INFO - \r\n",
      "backbone.layer6.0.conv.1.conv.weight - torch.Size([576, 1, 3, 3]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,532 - mmcv - INFO - \r\n",
      "backbone.layer6.0.conv.1.bn.weight - torch.Size([576]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,532 - mmcv - INFO - \r\n",
      "backbone.layer6.0.conv.1.bn.bias - torch.Size([576]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,532 - mmcv - INFO - \r\n",
      "backbone.layer6.0.conv.2.conv.weight - torch.Size([160, 576, 1, 1]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,532 - mmcv - INFO - \r\n",
      "backbone.layer6.0.conv.2.bn.weight - torch.Size([160]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,532 - mmcv - INFO - \r\n",
      "backbone.layer6.0.conv.2.bn.bias - torch.Size([160]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,532 - mmcv - INFO - \r\n",
      "backbone.layer6.1.conv.0.conv.weight - torch.Size([960, 160, 1, 1]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,532 - mmcv - INFO - \r\n",
      "backbone.layer6.1.conv.0.bn.weight - torch.Size([960]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,533 - mmcv - INFO - \r\n",
      "backbone.layer6.1.conv.0.bn.bias - torch.Size([960]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,533 - mmcv - INFO - \r\n",
      "backbone.layer6.1.conv.1.conv.weight - torch.Size([960, 1, 3, 3]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,533 - mmcv - INFO - \r\n",
      "backbone.layer6.1.conv.1.bn.weight - torch.Size([960]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,533 - mmcv - INFO - \r\n",
      "backbone.layer6.1.conv.1.bn.bias - torch.Size([960]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,533 - mmcv - INFO - \r\n",
      "backbone.layer6.1.conv.2.conv.weight - torch.Size([160, 960, 1, 1]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,533 - mmcv - INFO - \r\n",
      "backbone.layer6.1.conv.2.bn.weight - torch.Size([160]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,533 - mmcv - INFO - \r\n",
      "backbone.layer6.1.conv.2.bn.bias - torch.Size([160]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,533 - mmcv - INFO - \r\n",
      "backbone.layer6.2.conv.0.conv.weight - torch.Size([960, 160, 1, 1]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,533 - mmcv - INFO - \r\n",
      "backbone.layer6.2.conv.0.bn.weight - torch.Size([960]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,533 - mmcv - INFO - \r\n",
      "backbone.layer6.2.conv.0.bn.bias - torch.Size([960]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,533 - mmcv - INFO - \r\n",
      "backbone.layer6.2.conv.1.conv.weight - torch.Size([960, 1, 3, 3]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,533 - mmcv - INFO - \r\n",
      "backbone.layer6.2.conv.1.bn.weight - torch.Size([960]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,533 - mmcv - INFO - \r\n",
      "backbone.layer6.2.conv.1.bn.bias - torch.Size([960]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,533 - mmcv - INFO - \r\n",
      "backbone.layer6.2.conv.2.conv.weight - torch.Size([160, 960, 1, 1]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,533 - mmcv - INFO - \r\n",
      "backbone.layer6.2.conv.2.bn.weight - torch.Size([160]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,533 - mmcv - INFO - \r\n",
      "backbone.layer6.2.conv.2.bn.bias - torch.Size([160]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,533 - mmcv - INFO - \r\n",
      "backbone.layer7.0.conv.0.conv.weight - torch.Size([960, 160, 1, 1]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,533 - mmcv - INFO - \r\n",
      "backbone.layer7.0.conv.0.bn.weight - torch.Size([960]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,533 - mmcv - INFO - \r\n",
      "backbone.layer7.0.conv.0.bn.bias - torch.Size([960]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,533 - mmcv - INFO - \r\n",
      "backbone.layer7.0.conv.1.conv.weight - torch.Size([960, 1, 3, 3]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,533 - mmcv - INFO - \r\n",
      "backbone.layer7.0.conv.1.bn.weight - torch.Size([960]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,533 - mmcv - INFO - \r\n",
      "backbone.layer7.0.conv.1.bn.bias - torch.Size([960]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,533 - mmcv - INFO - \r\n",
      "backbone.layer7.0.conv.2.conv.weight - torch.Size([320, 960, 1, 1]): \r\n",
      "KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,533 - mmcv - INFO - \r\n",
      "backbone.layer7.0.conv.2.bn.weight - torch.Size([320]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,533 - mmcv - INFO - \r\n",
      "backbone.layer7.0.conv.2.bn.bias - torch.Size([320]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,533 - mmcv - INFO - \r\n",
      "backbone.conv2.conv.weight - torch.Size([1280, 320, 1, 1]): \r\n",
      "Initialized by user-defined `init_weights` in ConvModule  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,533 - mmcv - INFO - \r\n",
      "backbone.conv2.bn.weight - torch.Size([1280]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,533 - mmcv - INFO - \r\n",
      "backbone.conv2.bn.bias - torch.Size([1280]): \r\n",
      "The value is the same before and after calling `init_weights` of ImageClassifier  \r\n",
      " \r\n",
      "2023-02-03 23:28:49,533 - mmcv - INFO - \r\n",
      "head.fc.weight - torch.Size([30, 1280]): \r\n",
      "NormalInit: mean=0, std=0.01, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:49,534 - mmcv - INFO - \r\n",
      "head.fc.bias - torch.Size([30]): \r\n",
      "NormalInit: mean=0, std=0.01, bias=0 \r\n",
      " \r\n",
      "2023-02-03 23:28:50,888 - mmcls - INFO - load checkpoint from local path: mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth\r\n",
      "2023-02-03 23:28:50,911 - mmcls - WARNING - The model and loaded state dict do not match exactly\r\n",
      "\r\n",
      "size mismatch for head.fc.weight: copying a param with shape torch.Size([1000, 1280]) from checkpoint, the shape in current model is torch.Size([30, 1280]).\r\n",
      "size mismatch for head.fc.bias: copying a param with shape torch.Size([1000]) from checkpoint, the shape in current model is torch.Size([30]).\r\n",
      "2023-02-03 23:28:50,911 - mmcls - INFO - Start running, host: cine@cine-prof, work_dir: /home/cine/Documents/sjtu-openmmlab-tutorial-main/work_dirs/mobilenet-v2_fruit\r\n",
      "2023-02-03 23:28:50,911 - mmcls - INFO - Hooks will be executed in the following order:\r\n",
      "before_run:\r\n",
      "(VERY_HIGH   ) StepLrUpdaterHook                  \r\n",
      "(NORMAL      ) CheckpointHook                     \r\n",
      "(LOW         ) EvalHook                           \r\n",
      "(VERY_LOW    ) TextLoggerHook                     \r\n",
      " -------------------- \r\n",
      "before_train_epoch:\r\n",
      "(VERY_HIGH   ) StepLrUpdaterHook                  \r\n",
      "(LOW         ) IterTimerHook                      \r\n",
      "(LOW         ) EvalHook                           \r\n",
      "(VERY_LOW    ) TextLoggerHook                     \r\n",
      " -------------------- \r\n",
      "before_train_iter:\r\n",
      "(VERY_HIGH   ) StepLrUpdaterHook                  \r\n",
      "(LOW         ) IterTimerHook                      \r\n",
      "(LOW         ) EvalHook                           \r\n",
      " -------------------- \r\n",
      "after_train_iter:\r\n",
      "(ABOVE_NORMAL) OptimizerHook                      \r\n",
      "(NORMAL      ) CheckpointHook                     \r\n",
      "(LOW         ) IterTimerHook                      \r\n",
      "(LOW         ) EvalHook                           \r\n",
      "(VERY_LOW    ) TextLoggerHook                     \r\n",
      " -------------------- \r\n",
      "after_train_epoch:\r\n",
      "(NORMAL      ) CheckpointHook                     \r\n",
      "(LOW         ) EvalHook                           \r\n",
      "(VERY_LOW    ) TextLoggerHook                     \r\n",
      " -------------------- \r\n",
      "before_val_epoch:\r\n",
      "(LOW         ) IterTimerHook                      \r\n",
      "(VERY_LOW    ) TextLoggerHook                     \r\n",
      " -------------------- \r\n",
      "before_val_iter:\r\n",
      "(LOW         ) IterTimerHook                      \r\n",
      " -------------------- \r\n",
      "after_val_iter:\r\n",
      "(LOW         ) IterTimerHook                      \r\n",
      " -------------------- \r\n",
      "after_val_epoch:\r\n",
      "(VERY_LOW    ) TextLoggerHook                     \r\n",
      " -------------------- \r\n",
      "after_run:\r\n",
      "(VERY_LOW    ) TextLoggerHook                     \r\n",
      " -------------------- \r\n",
      "2023-02-03 23:28:50,912 - mmcls - INFO - workflow: [('train', 1)], max: 5 epochs\r\n",
      "2023-02-03 23:28:50,912 - mmcls - INFO - Checkpoints will be saved to /home/cine/Documents/sjtu-openmmlab-tutorial-main/work_dirs/mobilenet-v2_fruit by HardDiskBackend.\r\n",
      "2023-02-03 23:28:54,114 - mmcls - INFO - Epoch [1][10/137]\tlr: 5.000e-03, eta: 0:03:33, time: 0.316, data_time: 0.224, memory: 1710, loss: 3.3344\r\n",
      "2023-02-03 23:28:54,943 - mmcls - INFO - Epoch [1][20/137]\tlr: 5.000e-03, eta: 0:02:12, time: 0.083, data_time: 0.004, memory: 1710, loss: 2.5659\r\n",
      "2023-02-03 23:28:55,772 - mmcls - INFO - Epoch [1][30/137]\tlr: 5.000e-03, eta: 0:01:45, time: 0.082, data_time: 0.004, memory: 1710, loss: 1.7932\r\n",
      "2023-02-03 23:28:56,602 - mmcls - INFO - Epoch [1][40/137]\tlr: 5.000e-03, eta: 0:01:31, time: 0.083, data_time: 0.005, memory: 1710, loss: 1.4377\r\n",
      "2023-02-03 23:28:57,436 - mmcls - INFO - Epoch [1][50/137]\tlr: 5.000e-03, eta: 0:01:22, time: 0.083, data_time: 0.005, memory: 1710, loss: 1.2240\r\n",
      "2023-02-03 23:28:58,335 - mmcls - INFO - Epoch [1][60/137]\tlr: 5.000e-03, eta: 0:01:16, time: 0.090, data_time: 0.029, memory: 1710, loss: 1.1342\r\n",
      "2023-02-03 23:28:59,195 - mmcls - INFO - Epoch [1][70/137]\tlr: 5.000e-03, eta: 0:01:12, time: 0.086, data_time: 0.026, memory: 1710, loss: 0.9371\r\n",
      "2023-02-03 23:29:00,113 - mmcls - INFO - Epoch [1][80/137]\tlr: 5.000e-03, eta: 0:01:09, time: 0.091, data_time: 0.030, memory: 1710, loss: 1.0324\r\n",
      "2023-02-03 23:29:01,019 - mmcls - INFO - Epoch [1][90/137]\tlr: 5.000e-03, eta: 0:01:06, time: 0.091, data_time: 0.030, memory: 1710, loss: 0.8540\r\n",
      "2023-02-03 23:29:01,923 - mmcls - INFO - Epoch [1][100/137]\tlr: 5.000e-03, eta: 0:01:04, time: 0.091, data_time: 0.029, memory: 1710, loss: 0.8490\r\n",
      "2023-02-03 23:29:02,773 - mmcls - INFO - Epoch [1][110/137]\tlr: 5.000e-03, eta: 0:01:01, time: 0.085, data_time: 0.024, memory: 1710, loss: 0.9506\r\n",
      "2023-02-03 23:29:03,641 - mmcls - INFO - Epoch [1][120/137]\tlr: 5.000e-03, eta: 0:00:59, time: 0.087, data_time: 0.024, memory: 1710, loss: 0.8302\r\n",
      "2023-02-03 23:29:04,508 - mmcls - INFO - Epoch [1][130/137]\tlr: 5.000e-03, eta: 0:00:57, time: 0.087, data_time: 0.026, memory: 1710, loss: 0.9696\r\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>>] 1078/1078, 350.9 task/s, elapsed: 3s, ETA:     0s2023-02-03 23:29:08,233 - mmcls - INFO - Epoch(val) [1][34]\taccuracy_top-1: 77.4583, accuracy_top-5: 97.2171\r\n",
      "2023-02-03 23:29:11,381 - mmcls - INFO - Epoch [2][10/137]\tlr: 4.900e-03, eta: 0:01:00, time: 0.310, data_time: 0.248, memory: 1710, loss: 0.7066\r\n",
      "2023-02-03 23:29:12,290 - mmcls - INFO - Epoch [2][20/137]\tlr: 4.900e-03, eta: 0:00:59, time: 0.091, data_time: 0.031, memory: 1710, loss: 0.7298\r\n",
      "2023-02-03 23:29:13,191 - mmcls - INFO - Epoch [2][30/137]\tlr: 4.900e-03, eta: 0:00:57, time: 0.090, data_time: 0.029, memory: 1710, loss: 0.8092\r\n",
      "2023-02-03 23:29:14,224 - mmcls - INFO - Epoch [2][40/137]\tlr: 4.900e-03, eta: 0:00:55, time: 0.103, data_time: 0.042, memory: 1710, loss: 0.8284\r\n",
      "2023-02-03 23:29:15,189 - mmcls - INFO - Epoch [2][50/137]\tlr: 4.900e-03, eta: 0:00:54, time: 0.096, data_time: 0.035, memory: 1710, loss: 0.5523\r\n",
      "2023-02-03 23:29:16,111 - mmcls - INFO - Epoch [2][60/137]\tlr: 4.900e-03, eta: 0:00:52, time: 0.092, data_time: 0.031, memory: 1710, loss: 0.7898\r\n",
      "2023-02-03 23:29:16,993 - mmcls - INFO - Epoch [2][70/137]\tlr: 4.900e-03, eta: 0:00:51, time: 0.089, data_time: 0.027, memory: 1710, loss: 0.8132\r\n",
      "2023-02-03 23:29:17,896 - mmcls - INFO - Epoch [2][80/137]\tlr: 4.900e-03, eta: 0:00:49, time: 0.090, data_time: 0.029, memory: 1710, loss: 0.7942\r\n",
      "2023-02-03 23:29:18,788 - mmcls - INFO - Epoch [2][90/137]\tlr: 4.900e-03, eta: 0:00:48, time: 0.089, data_time: 0.028, memory: 1710, loss: 0.7241\r\n",
      "2023-02-03 23:29:19,656 - mmcls - INFO - Epoch [2][100/137]\tlr: 4.900e-03, eta: 0:00:47, time: 0.087, data_time: 0.026, memory: 1710, loss: 0.6991\r\n",
      "2023-02-03 23:29:20,511 - mmcls - INFO - Epoch [2][110/137]\tlr: 4.900e-03, eta: 0:00:45, time: 0.085, data_time: 0.023, memory: 1710, loss: 0.5920\r\n",
      "2023-02-03 23:29:21,392 - mmcls - INFO - Epoch [2][120/137]\tlr: 4.900e-03, eta: 0:00:44, time: 0.088, data_time: 0.028, memory: 1710, loss: 0.9069\r\n",
      "2023-02-03 23:29:22,254 - mmcls - INFO - Epoch [2][130/137]\tlr: 4.900e-03, eta: 0:00:43, time: 0.086, data_time: 0.022, memory: 1710, loss: 0.6859\r\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>>] 1078/1078, 371.1 task/s, elapsed: 3s, ETA:     0s2023-02-03 23:29:25,769 - mmcls - INFO - Epoch(val) [2][34]\taccuracy_top-1: 80.9833, accuracy_top-5: 96.9388\r\n",
      "2023-02-03 23:29:28,880 - mmcls - INFO - Epoch [3][10/137]\tlr: 4.802e-03, eta: 0:00:43, time: 0.306, data_time: 0.245, memory: 1710, loss: 0.7279\r\n",
      "2023-02-03 23:29:29,770 - mmcls - INFO - Epoch [3][20/137]\tlr: 4.802e-03, eta: 0:00:41, time: 0.089, data_time: 0.028, memory: 1710, loss: 0.6592\r\n",
      "2023-02-03 23:29:30,630 - mmcls - INFO - Epoch [3][30/137]\tlr: 4.802e-03, eta: 0:00:40, time: 0.086, data_time: 0.023, memory: 1710, loss: 0.6828\r\n",
      "2023-02-03 23:29:31,462 - mmcls - INFO - Epoch [3][40/137]\tlr: 4.802e-03, eta: 0:00:39, time: 0.083, data_time: 0.020, memory: 1710, loss: 0.5941\r\n",
      "2023-02-03 23:29:32,325 - mmcls - INFO - Epoch [3][50/137]\tlr: 4.802e-03, eta: 0:00:37, time: 0.086, data_time: 0.031, memory: 1710, loss: 0.7314\r\n",
      "2023-02-03 23:29:33,265 - mmcls - INFO - Epoch [3][60/137]\tlr: 4.802e-03, eta: 0:00:36, time: 0.094, data_time: 0.041, memory: 1710, loss: 0.5549\r\n",
      "2023-02-03 23:29:34,141 - mmcls - INFO - Epoch [3][70/137]\tlr: 4.802e-03, eta: 0:00:35, time: 0.088, data_time: 0.025, memory: 1710, loss: 0.6377\r\n",
      "2023-02-03 23:29:35,043 - mmcls - INFO - Epoch [3][80/137]\tlr: 4.802e-03, eta: 0:00:34, time: 0.090, data_time: 0.028, memory: 1710, loss: 0.7133\r\n",
      "2023-02-03 23:29:35,934 - mmcls - INFO - Epoch [3][90/137]\tlr: 4.802e-03, eta: 0:00:33, time: 0.089, data_time: 0.027, memory: 1710, loss: 0.5053\r\n",
      "2023-02-03 23:29:36,823 - mmcls - INFO - Epoch [3][100/137]\tlr: 4.802e-03, eta: 0:00:32, time: 0.089, data_time: 0.025, memory: 1710, loss: 0.6386\r\n",
      "2023-02-03 23:29:37,669 - mmcls - INFO - Epoch [3][110/137]\tlr: 4.802e-03, eta: 0:00:30, time: 0.085, data_time: 0.020, memory: 1710, loss: 0.4967\r\n",
      "2023-02-03 23:29:38,551 - mmcls - INFO - Epoch [3][120/137]\tlr: 4.802e-03, eta: 0:00:29, time: 0.088, data_time: 0.027, memory: 1710, loss: 0.5476\r\n",
      "2023-02-03 23:29:39,394 - mmcls - INFO - Epoch [3][130/137]\tlr: 4.802e-03, eta: 0:00:28, time: 0.084, data_time: 0.017, memory: 1710, loss: 0.6236\r\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>>] 1078/1078, 367.2 task/s, elapsed: 3s, ETA:     0s2023-02-03 23:29:42,919 - mmcls - INFO - Epoch(val) [3][34]\taccuracy_top-1: 82.8386, accuracy_top-5: 98.1447\r\n",
      "2023-02-03 23:29:46,015 - mmcls - INFO - Epoch [4][10/137]\tlr: 4.706e-03, eta: 0:00:27, time: 0.305, data_time: 0.244, memory: 1710, loss: 0.4924\r\n",
      "2023-02-03 23:29:46,925 - mmcls - INFO - Epoch [4][20/137]\tlr: 4.706e-03, eta: 0:00:26, time: 0.091, data_time: 0.030, memory: 1710, loss: 0.4881\r\n",
      "2023-02-03 23:29:47,786 - mmcls - INFO - Epoch [4][30/137]\tlr: 4.706e-03, eta: 0:00:25, time: 0.086, data_time: 0.022, memory: 1710, loss: 0.5840\r\n",
      "2023-02-03 23:29:48,686 - mmcls - INFO - Epoch [4][40/137]\tlr: 4.706e-03, eta: 0:00:24, time: 0.090, data_time: 0.028, memory: 1710, loss: 0.5254\r\n",
      "2023-02-03 23:29:49,555 - mmcls - INFO - Epoch [4][50/137]\tlr: 4.706e-03, eta: 0:00:23, time: 0.087, data_time: 0.026, memory: 1710, loss: 0.5491\r\n",
      "2023-02-03 23:29:50,434 - mmcls - INFO - Epoch [4][60/137]\tlr: 4.706e-03, eta: 0:00:22, time: 0.088, data_time: 0.025, memory: 1710, loss: 0.5699\r\n",
      "2023-02-03 23:29:51,310 - mmcls - INFO - Epoch [4][70/137]\tlr: 4.706e-03, eta: 0:00:20, time: 0.087, data_time: 0.034, memory: 1710, loss: 0.5218\r\n",
      "2023-02-03 23:29:52,206 - mmcls - INFO - Epoch [4][80/137]\tlr: 4.706e-03, eta: 0:00:19, time: 0.090, data_time: 0.033, memory: 1710, loss: 0.4301\r\n",
      "2023-02-03 23:29:53,115 - mmcls - INFO - Epoch [4][90/137]\tlr: 4.706e-03, eta: 0:00:18, time: 0.091, data_time: 0.034, memory: 1710, loss: 0.5009\r\n",
      "2023-02-03 23:29:53,952 - mmcls - INFO - Epoch [4][100/137]\tlr: 4.706e-03, eta: 0:00:17, time: 0.084, data_time: 0.025, memory: 1710, loss: 0.4335\r\n",
      "2023-02-03 23:29:54,860 - mmcls - INFO - Epoch [4][110/137]\tlr: 4.706e-03, eta: 0:00:16, time: 0.091, data_time: 0.035, memory: 1710, loss: 0.5121\r\n",
      "2023-02-03 23:29:55,718 - mmcls - INFO - Epoch [4][120/137]\tlr: 4.706e-03, eta: 0:00:15, time: 0.086, data_time: 0.028, memory: 1710, loss: 0.4879\r\n",
      "2023-02-03 23:29:56,612 - mmcls - INFO - Epoch [4][130/137]\tlr: 4.706e-03, eta: 0:00:14, time: 0.089, data_time: 0.030, memory: 1710, loss: 0.3874\r\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>>] 1078/1078, 365.3 task/s, elapsed: 3s, ETA:     0s2023-02-03 23:30:00,164 - mmcls - INFO - Epoch(val) [4][34]\taccuracy_top-1: 86.9202, accuracy_top-5: 98.7941\r\n",
      "2023-02-03 23:30:03,271 - mmcls - INFO - Epoch [5][10/137]\tlr: 4.612e-03, eta: 0:00:13, time: 0.306, data_time: 0.244, memory: 1710, loss: 0.3870\r\n",
      "2023-02-03 23:30:04,191 - mmcls - INFO - Epoch [5][20/137]\tlr: 4.612e-03, eta: 0:00:12, time: 0.092, data_time: 0.031, memory: 1710, loss: 0.4727\r\n",
      "2023-02-03 23:30:05,064 - mmcls - INFO - Epoch [5][30/137]\tlr: 4.612e-03, eta: 0:00:11, time: 0.088, data_time: 0.027, memory: 1710, loss: 0.4021\r\n",
      "2023-02-03 23:30:05,936 - mmcls - INFO - Epoch [5][40/137]\tlr: 4.612e-03, eta: 0:00:09, time: 0.087, data_time: 0.025, memory: 1710, loss: 0.4790\r\n",
      "2023-02-03 23:30:06,810 - mmcls - INFO - Epoch [5][50/137]\tlr: 4.612e-03, eta: 0:00:08, time: 0.087, data_time: 0.026, memory: 1710, loss: 0.4317\r\n",
      "2023-02-03 23:30:07,710 - mmcls - INFO - Epoch [5][60/137]\tlr: 4.612e-03, eta: 0:00:07, time: 0.090, data_time: 0.028, memory: 1710, loss: 0.5619\r\n",
      "2023-02-03 23:30:08,591 - mmcls - INFO - Epoch [5][70/137]\tlr: 4.612e-03, eta: 0:00:06, time: 0.088, data_time: 0.027, memory: 1710, loss: 0.4022\r\n",
      "2023-02-03 23:30:09,488 - mmcls - INFO - Epoch [5][80/137]\tlr: 4.612e-03, eta: 0:00:05, time: 0.090, data_time: 0.026, memory: 1710, loss: 0.3518\r\n",
      "2023-02-03 23:30:10,321 - mmcls - INFO - Epoch [5][90/137]\tlr: 4.612e-03, eta: 0:00:04, time: 0.083, data_time: 0.009, memory: 1710, loss: 0.3738\r\n",
      "2023-02-03 23:30:11,154 - mmcls - INFO - Epoch [5][100/137]\tlr: 4.612e-03, eta: 0:00:03, time: 0.083, data_time: 0.005, memory: 1710, loss: 0.3728\r\n",
      "2023-02-03 23:30:12,009 - mmcls - INFO - Epoch [5][110/137]\tlr: 4.612e-03, eta: 0:00:02, time: 0.086, data_time: 0.024, memory: 1710, loss: 0.5980\r\n",
      "2023-02-03 23:30:12,890 - mmcls - INFO - Epoch [5][120/137]\tlr: 4.612e-03, eta: 0:00:01, time: 0.088, data_time: 0.027, memory: 1710, loss: 0.4721\r\n",
      "2023-02-03 23:30:13,766 - mmcls - INFO - Epoch [5][130/137]\tlr: 4.612e-03, eta: 0:00:00, time: 0.088, data_time: 0.033, memory: 1710, loss: 0.4916\r\n",
      "2023-02-03 23:30:14,351 - mmcls - INFO - Saving checkpoint at 5 epochs\r\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>>] 1078/1078, 367.7 task/s, elapsed: 3s, ETA:     0s2023-02-03 23:30:17,402 - mmcls - INFO - Epoch(val) [5][34]\taccuracy_top-1: 86.5492, accuracy_top-5: 98.4230\r\n",
      "\u001B[32mTraining finished successfully. \u001B[0m\r\n"
     ]
    }
   ],
   "source": [
    "!mim train mmcls mobilenet-v2_fruit.py"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9da09fe8-4d5c-43d5-9456-0e7bcf8e740c",
   "metadata": {},
   "source": [
    "## Understand Logs\n",
    "\n",
    "\n",
    "The log is long but mainly contains the following parts:\n",
    "\n",
    "1. Toolbox information\n",
    "2. Dumped Config files\n",
    "3. Model Initialization Logs\n",
    "    1. Check `mmcls - INFO - load checkpoint from local path: mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth`, which means pretrained weights are loaded correctly.\n",
    "4. Information on Hooks: we don't configure this explicitly in this tutorial, so ignore that\n",
    "5. Training progress\n",
    "    1. Training logs: including current learning, training loss, time consumption, memory occupation\n",
    "    2. Validation logs: Accuracy on validation set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "515824b8-39f0-415f-8636-76f2cb7423f7",
   "metadata": {},
   "source": [
    "## Test the Model\n",
    "\n",
    "The trained model (checkpoint file) is usually saved under `work_dirs/{experiment_name}/latest.pth`. \n",
    "We can load it to test with a new image. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "75c30d1a-25d6-4fac-8aee-21003c6c8109",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cine/miniconda3/envs/mmlab1/lib/python3.8/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load checkpoint from local path: work_dirs/mobilenet-v2_fruit/latest.pth\n",
      "{'pred_label': 28, 'pred_score': 0.9998862743377686, 'pred_class': '香蕉'}\n"
     ]
    }
   ],
   "source": [
    "from mmcls.apis import init_model, inference_model\n",
    "\n",
    "model = init_model('mobilenet-v2_fruit.py', 'work_dirs/mobilenet-v2_fruit/latest.pth')\n",
    "result = inference_model(model, 'banana.png')\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cine/mmclassification/mmcls/core/visualization/image.py:268: UserWarning: Glyph 39321 (\\N{CJK UNIFIED IDEOGRAPH-9999}) missing from current font.\n",
      "  stream, _ = self.fig_save.canvas.print_to_buffer()\n",
      "/home/cine/mmclassification/mmcls/core/visualization/image.py:268: UserWarning: Glyph 34121 (\\N{CJK UNIFIED IDEOGRAPH-8549}) missing from current font.\n",
      "  stream, _ = self.fig_save.canvas.print_to_buffer()\n",
      "/home/cine/miniconda3/envs/mmlab1/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 39321 (\\N{CJK UNIFIED IDEOGRAPH-9999}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/cine/miniconda3/envs/mmlab1/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 34121 (\\N{CJK UNIFIED IDEOGRAPH-8549}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 393.01x403.01 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mmcls.apis import show_result_pyplot\n",
    "\n",
    "show_result_pyplot(model, 'banana.png', result)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "id": "1b36d5d6-bacf-4cf7-86f4-0b3b54230a73",
   "metadata": {},
   "source": [
    "## PyTorch codes under the hood"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3f17de7-87ab-4069-9961-dbdfb6615e26",
   "metadata": {},
   "source": [
    "### Runner\n",
    "\n",
    "Runner construct the framework of training.\n",
    "\n",
    "Specifically, MMClassification is based on `mmcv.EpochBasedRunner`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0afa5035-6b79-4c15-ba05-a06d339d7524",
   "metadata": {},
   "source": [
    "## PyTorch codes under the hood\n",
    "\n",
    "- train.py \n",
    "    - Process configs\n",
    "    - Build dataset object\n",
    "    - Build model object\n",
    "    - call `mmcls.apis.train_model(model, dataset, cfg, **kwargs)`\n",
    "        - Build dataloader object\n",
    "        - Build optimizer object\n",
    "        - Build runner via `runner.EpochBasedRunner(model, optimizer)`\n",
    "        - Call `runner.run(dataloader)`\n",
    "            - Fetch `batch` from `dataloader`\n",
    "                - Data augmentation via `train_pipeline`\n",
    "            - Call `losses = model.train_step(data_batch)`\n",
    "            - `loss.backward()` and `optimizer.step()` in throuth `mmcv.OptimizerHook.after_train_iter()` hook"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e61ab82e-f884-4133-b333-4d0736a9f1ea",
   "metadata": {},
   "source": [
    "`tools/train.py`\n",
    "```python\n",
    "def main():\n",
    "    model = build_classifier(cfg.model)\n",
    "    model.init_weights()\n",
    "\n",
    "    datasets = [build_dataset(cfg.data.train)]\n",
    "\n",
    "    train_model(\n",
    "        model,\n",
    "        datasets,\n",
    "        cfg,\n",
    "        distributed=distributed,\n",
    "        validate=(not args.no_validate),\n",
    "        timestamp=timestamp,\n",
    "        device=cfg.device,\n",
    "        meta=meta)\n",
    "```\n",
    "\n",
    "\n",
    "`mmcls/apis/train_model.py`\n",
    "```python\n",
    "def train_model(model,\n",
    "                dataset,\n",
    "                cfg):\n",
    "    \n",
    "    data_loaders = [build_dataloader(ds, **train_loader_cfg) for ds in dataset]\n",
    "\n",
    "    optimizer = build_optimizer(model, cfg.optimizer)\n",
    "    \n",
    "    runner = build_runner(\n",
    "        cfg.runner,\n",
    "        default_args=dict(\n",
    "            model=model,\n",
    "            optimizer=optimizer))\n",
    "    \n",
    "    runner.register_training_hooks(\n",
    "        cfg.lr_config,\n",
    "        optimizer_config,\n",
    "        cfg.checkpoint_config,\n",
    "        cfg.log_config,\n",
    "        cfg.get('momentum_config', None),\n",
    "        custom_hooks_config=cfg.get('custom_hooks', None))\n",
    "    \n",
    "    runner.run(data_loaders, cfg.workflow)\n",
    "```\n",
    "\n",
    "\n",
    "`mmcv/runner/epoch_based_runner.py`\n",
    "```python\n",
    "class EpochBasedRunner(BaseRunner):\n",
    "\n",
    "    def run_iter(self, data_batch: Any, train_mode: bool, **kwargs) -> None:\n",
    "        if train_mode:\n",
    "            outputs = self.model.train_step(data_batch, self.optimizer, **kwargs)\n",
    "        else:\n",
    "            outputs = self.model.val_step(data_batch, self.optimizer, **kwargs)\n",
    "\n",
    "        self.outputs = outputs\n",
    "\n",
    "    def train(self, data_loader, **kwargs):\n",
    "        self.model.train()\n",
    "        self.data_loader = data_loader\n",
    "        for i, data_batch in enumerate(self.data_loader):\n",
    "            self.run_iter(data_batch, train_mode=True, **kwargs)\n",
    "            self.call_hook('after_train_iter')\n",
    "```\n",
    "\n",
    "\n",
    "`mmcls/models/classifiers/base.py`\n",
    "```python\n",
    "class BaseClassifier(BaseModule, metaclass=ABCMeta):\n",
    "    \n",
    "    def forward(self, img, return_loss=True, **kwargs):\n",
    "        \"\"\"Calls either forward_train or forward_test depending on whether\n",
    "        return_loss=True.\n",
    "\n",
    "        Note this setting will change the expected inputs. When\n",
    "        `return_loss=True`, img and img_meta are single-nested (i.e. Tensor and\n",
    "        List[dict]), and when `resturn_loss=False`, img and img_meta should be\n",
    "        double nested (i.e.  List[Tensor], List[List[dict]]), with the outer\n",
    "        list indicating test time augmentations.\n",
    "        \"\"\"\n",
    "        if return_loss:\n",
    "            return self.forward_train(img, **kwargs)\n",
    "        else:\n",
    "            return self.forward_test(img, **kwargs)\n",
    "\n",
    "    def train_step(self, data, optimizer=None, **kwargs):\n",
    "        losses = self(**data)\n",
    "        loss, log_vars = self._parse_losses(losses)\n",
    "\n",
    "        outputs = dict(\n",
    "            loss=loss, log_vars=log_vars, num_samples=len(data['img'].data))\n",
    "\n",
    "        return outputs\n",
    "```\n",
    "\n",
    "\n",
    "`mmcls/models/classifiers/image.py`\n",
    "```python\n",
    "class ImageClassifier(BaseClassifier):\n",
    "    \n",
    "    def __init__(self,\n",
    "                 backbone,\n",
    "                 neck=None,\n",
    "                 head=None,\n",
    "                 pretrained=None,\n",
    "                 train_cfg=None,\n",
    "                 init_cfg=None):\n",
    "        super(ImageClassifier, self).__init__(init_cfg)\n",
    "\n",
    "        if pretrained is not None:\n",
    "            self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)\n",
    "        self.backbone = build_backbone(backbone)\n",
    "\n",
    "        if neck is not None:\n",
    "            self.neck = build_neck(neck)\n",
    "\n",
    "        if head is not None:\n",
    "            self.head = build_head(head)\n",
    "\n",
    "    def extract_feat(self, img):\n",
    "        x = self.backbone(img)\n",
    "\n",
    "        if self.with_neck:\n",
    "            x = self.neck(x)\n",
    "            \n",
    "        return x\n",
    "\n",
    "    def forward_train(self, img, gt_label, **kwargs):\n",
    "        x = self.extract_feat(img)\n",
    "\n",
    "        losses = dict()\n",
    "        loss = self.head.forward_train(x, gt_label)\n",
    "\n",
    "        losses.update(loss)\n",
    "\n",
    "        return losses\n",
    "```\n",
    "\n",
    "\n",
    "`mmcv/runner/hooks/optimizer.py`\n",
    "```python\n",
    "class OptimizerHook(Hook):\n",
    "    def after_train_iter(self, runner):\n",
    "        runner.optimizer.zero_grad()\n",
    "        runner.outputs['loss'].backward()\n",
    "        runner.optimizer.step()\n",
    "```"
   ]
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